5 Online Courses to Help You Get Started

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Want to become a machine learning master?

Don’t we all! Machine learning is hot right now, and it’s a quickly emerging field. Machine learning experts and similar data scientist roles are very much in demand. (If you prefer data science over ML, then check out 6 Key Data Science Concepts You Can Master Through Online Learning.)

To help kick-start your machine learning career, here are some great online courses and programs that will start to show you the inner workings of ML.

This course is offered online, so that students can make their own schedules while learning about the nuts and bolts of machine learning. Get a window into autonomous vehicle design, speech recognition technologies, automated web search and more of what machine learning has brought us within the last few years. There’s also a component on the Human Genome Project, where blending biology with machine learning has brought us some amazing advances in data handling.

This class will also show you how machine learning exists all around us. From medical diagnosis to recommendation engines, machine learning and neural networks are already a big part of our lives. In many cases, we don’t realize it because they are hidden behind the scenes. Illuminating many of the current use cases is an effective way to help beginners build ML knowledge.

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In addition, this course offers learning related to data mining, pattern recognition and various types of algorithm work. Learn the basics on supervised and unsupervised learning, as well as dimensionality reduction and other issues of dimensionality in machine learning praxis. All of this helps to prepare for a real role in ML implementation and design.

The Facts:

• Focus on machine learning, machine learning algorithms, artificial neural networks and logistic regression
• Single course
• Free enrolment, with an option to obtain a certificate for a fee

Duration: Approximately 55 hours to complete

Rating: 4.9 out of 5

These courses are a survey of higher-level machine learning that promise to enlighten the student on some of the inner workings of neural networks and similar technologies.

This specialization is all about how to take the mathematics behind machine learning and create a bridge to practical training technologies that will help you become proficient at developing the types of work that machine learning involves.

Multivariate calculus, dimensionality reduction and various components help students to become competent in these essential building blocks. The course requires some knowledge of Python as a programming language, and a basic understanding of the mathematics used in machine learning, including linear algebra.

The Facts:

• Focus on linear algebra, multivariable calculus, principal component analysis (PCA), and eigenvalues and eigenvectors
• 3 courses in this specialization
• Free enrolment, with an option to obtain a certificate for a fee

Duration: Approximately 2 months to complete (at suggested 12 hours per week)

Rating: 4.5 out of 5

This advanced level online specialization gets students closer to mastery of advanced practices such as deep learning and reinforcement learning.

Coursework will cover various types of machine learning goals and objectives, for example, natural language processing as well as computer vision, and how architectures like convolutional neural networks contribute to advances in image processing. Bayesian methods will also be treated in this course where scientists from CERN and Kaggle machine learning experts provide hands-on examples of implementing machine learning in the real world.

This specialization is billed as a program that allows students to begin to apply machine learning expertise in enterprise. That includes being able to better brainstorm the precise uses of enterprise machine learning and figuring out challenges and obstacles in real world implementations.

This type of practical specialization is inherently important in career employment later on, so this is an excellent choice for self-learning students to pursue at home. Being able to identify, as course writers say, the “caveats” of machine learning makes a career professional indispensable on a design team or in an advisory role. Machine learning is new, and companies are still adjusting and learning how best to apply these high-level technologies. (Or, if your interests lie in software development, check out 6 Software Development Concepts You Can Learn Through Online Courses.)

The Facts:

• Focus on machine learning, deep learning, data science, Bayesian methods, reinforcement learning, computer vision and natural language processing
• 7 courses in this specialization
• Free enrolment, with an option to obtain a certificate for a fee

Duration: Approximately 8 to 10 months to complete

Rating: 4.5 out of 5

Here’s a deep learning specialization that represents an intermediate-level machine learning class option.

These courses focus on deep learning and its relation to neural networks. Coursework will include various types of structures, such as convolutional neural networks, LSTM, recurrent neural networks and more. The course will also show how these apply to various industries including health care, natural language processing and manufacturing. You’ll see some of the basics of autonomous driving technologies at work, and utilize Python and TensorFlow to start building knowledge of machine learning models. All of this offers a solid foundation for going further into how ML is redefining automation in our world.

The Facts:

• Focus on deep learning, artificial neural networks, convolutional neural networks and TensorFlow
• 5 courses in this specialization
• Free enrolment, with an option to obtain a certificate for a fee

Duration: Approximately 3 months to complete (at suggested 11 hours per week)

Rating: 4.9 out of 5

These courses specialize in some of the most common core technologies used to implement machine learning in today’s enterprise.

Here, educators are looking at introducing machine learning to students in a profound way and going over specific use cases. This specialization will answer questions about the popularity of neural networks, as well as supervised and unsupervised machine learning models, gradient descent, and test and training data sets.

This specialization focuses on the use of TensorFlow and a specific type of cloud model based on Google offerings as students get hands-on experience with AI and machine learning.

The Facts:

• Focus on machine learning, TensorFlow, cloud computing and feature engineering
• 5 courses in this specialization
• Free enrolment, with an option to obtain a certificate for a fee

Duration: Approximately 1 month to complete (at suggested 15 hours per week)

Rating: 4.6 out of 5

Use any of these available online courses to get started in machine learning, and work toward a rewarding career in a high-tech role.